A Detailed Look at Scale and Translation Invariance in a Hierarchical Neural Model of Visual Object Recognition

The HMAX model has recently been proposed by Riesenhuber & Poggio as a hierarchical model of position- and size-invariant object recognition in visual cortex. It has also turned out to model successfully a number of other properties of the ventral visual stream (the visual pathway thought to be crucial for object recognition in cortex), and particularly of (view-tuned) neurons in macaque inferotemporal cortex, the brain area at the top of the ventral stream. The original modeling study only used ``paperclip'' stimuli, as in the corresponding physiology experiment, and did not explore systematically how model units' invariance properties depended on model parameters. In this study, we aimed at a deeper understanding of the inner workings of HMAX and its performance for various parameter settings and ``natural'' stimulus classes. We examined HMAX responses for different stimulus sizes and positions systematically and found a dependence of model units' responses on stimulus position for which a quantitative description is offered. Interestingly, we find that scale invariance properties of hierarchical neural models are not independent of stimulus class, as opposed to translation invariance, even though both are affine transformations within the image plane.

Hierarchical Mixtures of Experts and the EM Algorithm

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.

Statistical Models for Co-occurrence Data

Modeling and predicting co-occurrences of events is a fundamental problem of unsupervised learning. In this contribution we develop a statistical framework for analyzing co-occurrence data in a general setting where elementary observations are joint occurrences of pairs of abstract objects from two finite sets. The main challenge for statistical models in this context is to overcome the inherent data sparseness and to estimate the probabilities for pairs which were rarely observed or even unobserved in a given sample set. Moreover, it is often of considerable interest to extract grouping structure or to find a hierarchical data organization. A novel family of mixture models is proposed which explain the observed data by a finite number of shared aspects or clusters. This provides a common framework for statistical inference and structure discovery and also includes several recently proposed models as special cases. Adopting the maximum likelihood principle, EM algorithms are derived to fit the model parameters. We develop improved versions of EM which largely avoid overfitting problems and overcome the inherent locality of EM--based optimization. Among the broad variety of possible applications, e.g., in information retrieval, natural language processing, data mining, and computer vision, we have chosen document retrieval, the statistical analysis of noun/adjective co-occurrence and the unsupervised segmentation of textured images to test and evaluate the proposed algorithms.

Paral·lelització especulativa de programes sobre un clúster

Estudi sobre la millora de rendiment (en temps d’execució) a l'algorisme de gràfics Fast Multipath Radiosity Using Hierarchical Subscenes gràcies a l’execució paral•lela especulada que ens permet obtenir el motor d'especulació per a clústers desenvolupat en el grup de recerca BCDS de la Universitat de Girona

Global and dominance with concentric hierarchical stimuli and orientation classification task. 'Dominancia global y local con est??mulos conc??ntricos y tareas de clasificaci??n de la orientaci??n'

Resumen tomado de la publicaci??n. Resumen tambi??n en ingl??s

On AUV control architecture

This paper surveys control architectures proposed in the literature and describes a control architecture that is being developed for a semi-autonomous underwater vehicle for intervention missions (SAUVIM) at the University of Hawaii. Conceived as hybrid, this architecture has been organized in three layers: planning, control and execution. The mission is planned with a sequence of subgoals. Each subgoal has a related task supervisor responsible for arranging a set of pre-programmed task modules in order to achieve the subgoal. Task modules are the key concept of the architecture. They are the main building blocks and can be dynamically re-arranged by the task supervisor. In our architecture, deliberation takes place at the planning layer while reaction is dealt through the parallel execution of the task modules. Hence, the system presents both a hierarchical and an heterarchical decomposition, being able to show a predictable response while keeping rapid reactivity to the dynamic environment

How to convert a Word 2003 document into HTML

How to convert Word 2003 into HTML with associated resources folder. Includes examples showing how to create simple navigation within the document.

Anàlisi del document recopilatori de dades referides a la cobertura de la necessitat d'alimentació a Mallorca 2009. 'Análisis del documento recopilatorio de datos referidos a la cobertura de la necesidad de alimentación en Mallorca 2009'.

Resumen tomado del autor

L'ús del document arqueològic en l'ensenyament de la prehistòria. 'El uso del documento arqueológico en la enseñanza de la prehistoria'.

Resumen tomado de la publicación

L'escola narrada : el diari d'una mestra com a document de reflexió sobre la pràctica. 'La escuela narrada : el diario de una maestra como documento de reflexión sobre la práctica'.

Resumen tomado de la propia revista

Printing only part of an MS Word 2010 document

You won't always want to print the whole of your document; here are some useful ways of printing only part of a MS Word 2010 file. For best viewing Download the video.

Using the Document Map in MS Word 2011

The Document Map makes it very easy get an overview of the heading structure of a long document and to navigate aroiund the file quickly. If you watch this video also watch the ones on Using Heading and Contents Styles as well as on Tables of Content. For best viewing Download the video.

SYS-MDSTU Test document

This is a test document, and should be visible to 6 test students only. If showing correctly, the file will appear as a blank word doc.